Geographia Technica, Vol. 15, Issue 1, 2020, pp 16 to 26

GIS-BASED FLOOD HAZARD MAPPING USING HEC-RAS MODEL:

A CASE STUDY OF LOWER MEKONG RIVER, CAMBODIA

Vanthan KIM1, Sarintip TANTANEE2, Wayan SUPARTA3

DOI: 10.21163/GT_2020.151.02

ABSTRACT:

Rivers are the main water sources for human and animals’ lives. Unfortunately, they have

been frequently damaged by flooding. Flooding has affected and threatened not only

human’s lives and infrastructures but also the environmental capital. This study aims to

determine the application of flood frequency analysis integrated with the GIS and HEC-

RAS models to prepare a multi-return period flood hazard map in the Lower Mekong River

of Cambodia. A 30-year peak discharge (Kampong Cham gauging station) with a multi-

return period of 10, 20, 50, and 100-years was estimated by using four distributions

analysis. An EasyFit software is used to test the best distribution for the input of the HEC-

RAS model to prepare the estimation of the corresponding floodplain areas. The results

showed that Log-Pearson III distribution analysis of the return period of 10, 20, 50, 100

years is the best fits with the 52,208 m3/s, 54,990 m3/s, 59,381 m3/s, and 62,194 m3/s,

respectively. The HEC-RAS calibration indicated a good agreement with observed data

discharge 2011 and 2013. While the simulation model shows the return period of floods 10

and 20 years for the predicted depth of flooding is stable compared to the flood peaks of

2011 and 2013 discharges, but the conditions of other flood return periods are not stable.

Overall, HEC-RAS with its flood hazard map is a model that can estimate the level of flood

depth in the Lower Mekong River, Cambodia and is useful in providing information about

the depth and characteristics of floods for river communities.

Key-words: Flood Hazard Map, GIS, HEC-RAS, Flood Frequency analysis, Mekong River

1. INTRODUCTION

Over the last decades, the flood has been the most common natural disaster worldwide,

constructing many negative environmental and socio-economic consequences on people,

infrastructures, properties, and indirectly impact the country’s economy (Kheradmand et

al., 2018). The conservative flood management approach focusing on structural flood

mitigation measures have now been shifted to a risk-based flood mitigation concept

(Romali et al., 2018). Furthermore, the severity of flood hazard around the world requires

to continue prevention to reduce their impact (Azouagh et al., 2018). Because it is the most

widespread, frequent, and costly natural disaster for human societies (Mihu-Pintilie et al.,

2019). Each year, more than 140 million people across the world are affected by floods

(OECD, 2016). Flood hazard is the probability of a flood event will take place (Vojtek &

Vojtekova, 2016).

Flood modeling is very important for flood hazard assessment to show the magnitude

of a flood with a convincing exceeded probability (Azouagh et al., 2018), while the purpose

1,2 Naresuan University, Department of Civil Engineering, Faculty of Engineering, Phitsanulok 6500, Thailand,

Corresponding author: sarintipt@nu.ac.th 3 Universitas Pembangunan Jaya, Department of Informatics, South Tangerang City, Banten 15413,

Indonesia, Corresponding author: wayan.suparta@upj.ac.id ORCID 0000-0002-6193-1867

http://dx.doi.org/10.21163/GT_2020.151.02mailto:sarintipt@nu.ac.thmailto:wayan.suparta@upj.ac.idhttps://orcid.org/0000-0002-6193-1867

Vanthan KIM, Sarintip TANTANEE and Wayan SUPARTA / GIS-BASED FLOOD HAZARD … 17

of a vulnerability assessment is to provide hydrological characteristics to model the damage

(Rahmati et al., 2016). Some researchers (e.g.,Shafapour et al., 2017; Mihu-Pintilie & Nicu,

2019) applied a GIS-based approach to conduct flood hazard mapping with different

parameters (i.e. land use, land cover, DEM, soil, river network, and slop). Mostly, coupling

GIS and hydraulic models (Haidu, 2016) have been recommended for studying flood

analysis and flood prediction (Győri et al., 2016; Haidu et al., 2017; Vojtek et al., 2019). A

combination of GIS (Geographic Information System) and HEC-RAS (Hydrologic

Engineering Center-River Analysis System) has a great capability in the simulation of flood

hazard maps. HEC-RAS is one of the most commonly used models to analyze channel

flow and floodplain delineation (Maskong, 2019). River flood hazard mapping was first

initiated in 1988 in the United States by the Hydrologic Engineering Centre (HEC) of the

U.S. Army Corps of Engineers (USACE, 2018). The HEC-RAS model was found to give a

good performance where the simulated results for both studies showed a close agreement

with observed water surfaces.

In Cambodia, floods caused by the Mekong River in 2000 and 2011 killed 250 people,

affected 350,000 households of over 1.5 million people, causing 52,000 households to be

evacuated, costing the economy 521,000 million US dollars. These floods were ranked as

the worst natural disasters in Cambodia over the last 70 years (CFE-DM, 2017). Moreover,

report the Cambodian floods of 2013, which affected 20 out of 24 provinces, 377,354

households, claiming 168 lives, and forcing 31,314 households to be evacuated to safer

areas (Rishiraj et al., 2015; Vichet et al., 2019). Mochizuki et al. (2015), who study the

assessment of the natural disaster of flood and cyclone risks to public and private buildings

including educational structures, health facilities, and housing, estimates that the total direct

economic damage ranges from approximately 304 million US dollars for a 5-year return

period event, to 2.26 billion US dollars for a 1000-year return period event. Furthermore,

the annual records by the National Committee of Disaster Management of Cambodia (1996

to 2018) as well as (Yu et al., 2019) review of CRED, 2014, showed that extreme flooding

from the Mekong River mostly affected the country in 1978, 1991, 1994, 1996, 2000-2002,

2011, and 2013. Likewise, Cambodia Disaster and Risk Profile (EM-DAT) 2017, who

study floods caused by drought and storm based on frequency, mortality and economic

issues, observe that flooding induced more complicated impact than drought and storm

(CRED, 2019).

Flood hazard map is considered an important tool for tackling these problems. The

study aims to prepare a flood hazard map that integrates flood inundation areas, flood

extent, and flood depth in the study area. HEC-RAS and GIS-based methods are the main

components in analyzing flood hazards of the lower Mekong River, Cambodia. The scope

of the study is focused on the river floods, where investigate the ability of methods applied

to design flood hazard maps and the length of the data series.

2. STUDY AREA AND DATA

Cambodia is one of the countries in South-East Asia located between 102.350 and

107.620 longitude and 9.910 and 14.690 latitudes. The total area is 181,035 km2, 97.5

percent of which is the land while 2.5 percent is a water body (Vichet et al., 2019). The

Mekong River is one of the world’s longest river systems, flowing 4,909 km through six

countries: China, Myanmar, Thailand, Lao PDR, Cambodia, and Vietnam, having a basin

area of 795,000 km2, and a mean annual discharge of 14,500 m3/s or 475 km3/ year. The

flows are of a very large difference during the wet (June to October) and dry (November to

May) seasons (Ang & Oeurng, 2018).

18

This study aims to develop a return period-based flood hazard map using the GIS and

HEC-RAS models in the part of Kampong Cham (1,258 km2), Tboung Khmum (87 km2),

and Kandal (518 km2) province and Phnom Penh (85 km2) city. The river division (Fig. 1)

starts from the Mekong upstream Kampong Cham (KC) and reaches Chruy Changvar (CC)

gauging station (Phnom Penh City), with the area of 1,948 km2 and length 103.53 km in

Cambodia.

Fig. 1. Location of the study area (Source: authors).

Peak discharge in 30 years is collected from the Department of Meteorology and River

Works, Cambodia. The peak discharge is used to calculate the different return periods of

10, 20, 50, and 100-years. Hence, the 30m resolution of Advanced Spaceborne Thermal

Emission and Radiometer (ASTER) Digital Elevation Model (DEM) was downloaded free

from the U.S Geological Survey website (https://earthexplorer.usgs.gov/) in order to extract

basin geometry, stream networks, river geometry, Triangular Irregular Network (TIN), and

100 cross-sections.

3. METHODOLOGY

3.1. Flood Frequency Analysis

To analyze the extreme values of different return periods 10, 20, 50, and 100-years

using observed maximum historical discharge, a variety of methods can be applied, i.e.

Log-Pearson type III, Log-Normal, Normal, and Gumbel’s distribution (Tanaka et al.,

2017; Farooq et al., 2018; Bhat et al., 2019). Moreover, the estimation of peak discharge is

an important step for selecting flood events and different return periods to input model

processing.

https://earthexplorer.usgs.gov/

Vanthan KIM, Sarintip TANTANEE and Wayan SUPARTA / GIS-BASED FLOOD HAZARD … 19

In this study, Log-Pearson type III, Log-Normal, Normal, and Gumbel’s distribution

were used in flood frequency analysis. The EasyFit software was used to select the base

flood value to identify the peak flood of various historical records. 30 annual peak

discharges of Kampong Cham station (ID: 198,02 and coordinate, X: 551,341, Y:

1,327,363) between 1989 and 2018 were used. The goodness of fit test (GOF) of

Kolmogorov, Anderson, and Chi-Squared were employed to analyze and estimate the best-

fitted distribution.

3.2. GIS and HEC-RAS Modeling

GIS provides a broad range of tools for determining areas affected by floods or for

forecasting areas likely to be flooded due to high river water levels (Klemešová et al.,

2014). A DEM offers the most common way of showing topographic information and even

enables the modeling of flow across topography; a controlling factor in distributed models

of landform processes (Toosi et al., 2019).

HEC-RAS is a widely used hydraulic software tool developed by the U.S Army Corps

of Engineers (USACE, 2018). HEC-RAS employs 1-D flood routing in both steady and

unsteady flow conditions by applying an implicit-forward finite difference scheme between

successive sections of flexible geometry. The steady flow scheme is based on the solution

of the 1-D energy equation or the momentum equation between two successive cross-

sections (USACE, 2018). The energy equation is written as follows (Echogdali et al., 2018,

p. 963):

𝑍2 + 𝑌2 +𝑎2𝑉2

2

2𝑔= 𝑍1 + 𝑌1 +

𝑎1𝑉22

2𝑔+ ℎ𝑒 (1)

where Z1 and Z2 are the elevations of the main channel inverts, Y1 and Y2 are the depths of water at

cross-sections, V1, V2 is the average velocities (total discharges/total flow area), a1, a2 are the velocity

weighting coefficients, that account for non-uniformity of the velocity distribution over the cross-

section, g: gravitational acceleration, and he: is the energy head loss.

The cross-section sub-division for the water conveyance is calculated within each

reach using the following equations:

Q = KSf1,2, while K =

1.486

nAR2/3 (2)

where K = conveyance for subdivision, n = Manning roughness coefficient, A = flow area

subdivision, R = hydraulic radius for subdivision (wetted area/wetted perimeter), and Sf = friction

slope.

DEM was used as input data to generate a watershed and drainage network in RAS

Mapper. The channel, bank stations, flow direction, and cross-section cut lines were

prepared in RAS Mapper and exported to the HEC-RAS model. An upstream (Kampong

Cham) station of Lower Mekong River was selected for data input. Moreover, the multi

return periods of the peak floods were obtained from Log-Pearson III and used as an input

to the model in order to simulate results for each cross-section. At the same time, water

surface profiles were run in the model for 10, 20, 50, and 100-years. After running input

data in the HEC-RAS model, the outputs were exported to GIS in the format of the RAS

GIS Export file. GIS was used to generate flood depth mapping for multi return periods.

The overall methodology flow chart is shown in (Fig. 2).

20

Fig. 2. Methodology framework of flood hazard mapping

3.3 Calibration of HEC-RAS Model

The calibration of the model has used three indicators including Nash-Sutcliffe model

efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R2) were

computed using daily average flow, as standard show in (Table 1) (USACE, 2018)

NSE  =  1 − 

∑ (Qobsi−Qsimi

)2

n

i=1

∑ (Qobsi −Qobsi

)2

n

i=1

(3)

𝑃𝐵𝐼𝐴𝑆 = [∑ (𝑄𝑖

𝑜𝑏𝑠−𝑄𝑖𝑠𝑖𝑚)

𝑛

𝑖=1×100

∑ (𝑄𝑖𝑜𝑏𝑠)

𝑛

𝑖=1

] (4)

𝑅2 =∑((𝑄𝑠𝑖𝑚(𝑡)−𝑄𝑠𝑖𝑚)(𝑄𝑜𝑏𝑠(𝑡)−𝑄𝑜𝑏𝑠))

2

∑(𝑄𝑠𝑖𝑚(𝑡)−𝑄𝑠𝑖𝑚)2

∑(𝑄𝑜𝑏𝑠(𝑡)−𝑄𝑜𝑏𝑠)2 (5)

ASTER DEM

Digitizing

- DEM Project UTM 48N - River Network

- River Banks

- Flow Paths -Cross Section

HEC-RAS Import

- Create Project - Import Digitizing Data

- Compute HEC-RAS

- Check Results

Run HEC-RAS

Enough Cross

Section?

Yes

Flood Hazard Map

RAS to GIS Export

No

RAS Mapper

Processing

Frequency Analysis

10, 20, 50, 100 yr

Vanthan KIM, Sarintip TANTANEE and Wayan SUPARTA / GIS-BASED FLOOD HAZARD … 21

where Q sim(t) and Q obs(t) are the simulated and observed discharges at time step t, and

𝑄𝑠𝑖𝑚 𝑎𝑛𝑑 𝑄𝑜𝑏𝑠 are the simulated and observed average discharges. Table 1.

Performance ratings for summary statistics.

Source: US Army Corps of Engineers (USACE, 2018)

4. RESULTS AND DISCUSSIONS

4. 1 Flood Frequency Analysis

The peak discharge for 10, 20, 50, and 100-year return periods, is calculated using

Log-Pearson III, Log-Normal, Normal and Gumbel distributions as indicated in Table 2.

The Easyfit software found that the value of predicted peak flood using Log-Pearson 3

distribution is the best goodness of fit. The predicted maximum flood using Gumbel’s is the

highest as, compared to Log-N and Normal. The smallest values were obtained by Log-

Pearson III. Table 2.

Return periods based on Log-P3, Log-N, Normal, and Gumbel distributions analysis.

Return Period

(Years)

Estimated Peak Discharge in Deference Distribution at KC Station (m3/s)

Log-P3 Log-Normal Normal Gumbel

10 52208 50701 50242 51523

20 54990 55158 53698 55160

50 59381 57459 55376 59869

100 62194 61510 58171 63397

Table 3.

The performance ranking based on Kolmogorov, Anderson and Chi-Squared goodness of fit Test.

Table 3 indicates the performance ranking based on Kolmogorov, Anderson and Chi-

Squared test. Log-Pearson III is ranked first in terms of performance, followed by Log-N,

Normal, and Gumbel distribution. The ranking is based on the p-value. A p-value closer to

1 indicates a goodness of fit distribution. The highest p-value of goodness of fit test is

0.1419 and the lowest is 0.0933. Based on the results (Fig. 3), Log-Pearson III distribution

was put into HEC-RAS hydraulic model. The peak flood estimated for 10, 20, 50, and 100-

years are 52,208 m3/s, 54,990 m3/s, 59,381 m3/s, and 62,194 m3/s respectively of Kampong

Cham gauge station.

Performance Rating NSE PBIAS R2

Very Good 0.65 < NSE ≤ 1.00 PBIAS < ±15 0.65 < R2 ≤ 1.00

Good 0.55 < NSE ≤ 0.65 ±15 ≤ PBIAS < ±12 0.55 < R2 ≤ 0.65

Satisfactory 0.40 < NSE ≤ 0.55 ±20 ≤ PBIAS < ±30 0.70 < R2 ≤ 0.55

Unsatisfactory NSE ≤ 0.40 PBIAS ≥ ±30 R2 ≤ 0.40

Distribution Kolmogorov Anderson Chi-Squared

Statistic Rank Statistic Rank Statistic Rank

Log-Pearson III 0.0933 1 0.3371 1 0.2096 1

Normal 0.1053 2 0.4226 2 0.5162 2

Lognormal 0.1096 3 0.5972 3 2.0160 3

Gumbel Max 0.1419 4 1.4984 4 3.6242 4

22

P-P Plot

Log-Pearson 3

P (Empirical)

10.80.60.40.2

P (M

odel

)

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Fig. 3. Plot delineation goodness best fit of Log-Pearson III.

4.2 Performance Calibrated Model Simulation of Year 2011 and 2013

During the 2011 and 2013 flood events, there was a recorded highest hydrograph at the

study area. The peak discharge of the observed hydrograph in KC upstream was 50,967

m3/s, whereas that in CC downstream was only 39,612 m3/s. Based on this approach and

simulation, an upstream hydrograph was generated and the recorded hydrograph of the

downstream from the HEC-RAS model to validate the hydrograph recorded at the CC

station. These results were confirmed to correct this flood hydrograph; the new hydrograph

was the simulated during the years 2011 and 2013 to adjust the peak observed hydrograph

showed in (Fig. 4 and Fig. 5).

Fig. 4. Observed and simulated flow hydrograph at the downstream year 2011.

Fig. 5. Observed and simulated flow hydrograph at the downstream year 2013.

0

10000

20000

30000

40000

50000

1-May-11 1-Jul-11 1-Sep-11 1-Nov-11 1-Jan-12

Flo

w i

n m

3/s

Flow duration in daily

Observed

Simulation

R² = 0.97

0

10000

20000

30000

40000

50000

0 10000 20000 30000 40000 50000

Med

ol

Q (

m3/s

)

Observed Q (m3/s)

0

10000

20000

30000

40000

50000

1-May-13 1-Jul-13 1-Sep-13 1-Nov-13 1-Jan-14

Flo

w i

n m

3/s

Flow duration in daily

Observed

Simulation

R² = 0.9635

0

10000

20000

30000

40000

50000

0 10000 20000 30000 40000 50000

Mod

el Q

(m

3/s

)

Observed (m3/s)

Vanthan KIM, Sarintip TANTANEE and Wayan SUPARTA / GIS-BASED FLOOD HAZARD … 23

Hydraulic model performance (Table 4) was tested using NSE, PBIAS, and R2

statistics with values of the year 2011 (0.91, 15.14, and 0.97) and the year 2013 (0.90,

15.38, and 0.96), respectively. The calibration with simulated flood depths from the HEC-

RAS flow model shows a fairly good agreement with observations where their relationship

shows very strong.

Table 4.

Model performance of the river discharge at the stations during calibration.

Years Simulation period

Roughness

coefficient

Manning’s n

Boundary

condition

Normal depth

NSE PBIAS R2

2011 May-Dec, 2011 0.035 0.001 0.91 15.14 0.97

2013 May-Dec, 2013 0.035 0.001 0.90 15.38 0.96

4. 3 Flood Hazard Mapping

An HEC-RAS hydraulic modeling set-up was created to generate the water discharge

due to the 2011 and 2013 flood and subsequently, the flood map for various returns

simulated the 2011 and 2013 flood and the simulated 10, 20, 50 and 100-year periods. The

comparison between the return periods is presented. The flood depth was reclassified to

three levels such as 0.001 to 3 meters, 3 to 6 meters, 6 to 9 meters, and 9 to 14 meters, to

identify little or no flood, medium flood, and high flood events. The results are presented in

Tabel 5.

Table 5.

Flood depth extend the area of the return period 10, 20, 50, and 100-year.

Flood Depth (m) Flood depth deference return periods (RP) study area (km2)

10-year 20-year 50-year 100-year

< 3 (m) very low 156 137 131 128

3-6 (m) low 465 423 375 348

6-9 (m) medium 741 727 788 812

9-12 (m) high 294 371 358 353

> 12 (m) very high 21 27 45 66

The following maps are the simulation of the results steady from flood return

period 10, 20, 50, and 100-year, indicated as increase like (Fig. 6). The presented is

classified as the layer based on depth values as per the criteria mentioned in Table 3. For

floodplain exposure to the simulated flood depth and extent, ‘Intersect’ flood depth layer

(vector format) with the flood depth layer. Then summarize the exposed flood depth in the

form of graphs/maps while the river depth increases the highest from return period 10, 20,

50, and 100 as like 12.86 m, 13.10 m, 13.46 m, and 13.69 m. The following graph shows

landcover exposure to the deference of flood return period 10, 20, 50, and 100-year events

in the study.

24

Fig. 6. Simulated flood depth area at the return period

Q10yr=52208 m3/s, Q20yr= 54990 m3/s, Q50yr= 59381 m3/s, and Q100yr= 62194 m3/s.

5. CONCLUSIONS

The study is attempted to apply the HEC-RAS version 5.0.7 and GIS version 10.5 with

peak discharge through a steady flow analysis. Thus, the output of modeling was generated

into flood extent and flood depth in ArcGIS. Flood hazard maps from 10, 20, 50, and 100-

year return period flood with the value of affected areas and flood depth along with the

river study. According to the performance simulating of 2011 and 2013 with the

downstream station, the value of NSE, PBIAS, and R2 statistics with values of the year

2011 (0.91, 15.14, and 0.97) and year 2013 (0.90, 15.38, and 0.96) accuracy.

To construct a flood hazard map for the highest flood-affected area is the applicated

model and validation value of Manning’s n 0.035 for simulating 1D flood depth. Simulated

flood hazard map based on input peak discharge of multi flood return periods confirms that

the simulated flood hazard areas at 10, 20, 50, and 100-years are 52208 m3/s, 54990 m3/s,

59381 m3/s, and 62194 m3/s almost identical to the 2011 and 2013 (50295 m3/s and 50295

m3/s) observed peak discharge. The simulation suggests that most of the flood depth areas

of the 10 and 20-years flood return periods were also affected by the 2011 and 2013

historical floods. But 50 and 100-year flood return periods, the simulation was unstable.

The flood hazard map can be utilized as a tool to identify the priority of the area for the

planning of flood prevention, flood mitigation, and flood risk management.

This study presents the methodology for improving the awareness of the flood events.

The aim was to reduce the damage from floods and provide a better quality of life in the

Vanthan KIM, Sarintip TANTANEE and Wayan SUPARTA / GIS-BASED FLOOD HAZARD … 25

study area. Coupling of GIS and hydraulic modeling provides a solution to sustainable

flood protection and ensure a cleaner and safer environment. The present study mentions

the successful combination of scientific and practical experiences to show the effectiveness

of modeling techniques for engineering practice. In other words, it presents a successful

functioning system of flood mitigation measures that increases sustainability and

environmental protection of the territory. The outcome of the study could serve as an

essential basis for a more informed decision and science-based recommendations in

identifying river location and forming more effective policies in dealing with flood hazards.

ACKNOWLEDGEMENTS

The first author received the scholarship for his master’s degree from the Royal

Scholarship Project, under Her Royal Highness Princess Sirindhorn. I would like to express

gratitude to Naresuan University, Thailand and Universitas Pembangunan Jaya, Indonesia

for their provision of this special educational opportunity.

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